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In the last decades, wearable powered orthoses have been introduced in the state of the art for lower-limb rehabilitation. Most of these applications are driven by electric motors. Comparing with electric motor actuators, pneumatic artificial muscle (PAM) actuators are compliant because of the elasticity of PAMs. Consequently, for more safety and comfort of lower-limb rehabilitation, a compliant robotic orthosis powered by PAMs is developed. Based on safe control, a new control method, proxy-based sliding mode control (PSMC), was introduced into rehabilitation robotics a few years ago. It combined safety and accuracy of tracking to make it suitable for the safe control of PAM actuators. As the reason of low frequency response of PAM actuators and variable loads caused by different human subjects, the fixed parameters of PSMC makes the tracking performance vary from subject to subject, and lacks robustness. This paper presents a modification of PSMC by using neural network to tune PSMC gains online, and implements both PSMC and modified PSMC control schemes in the robotic orthosis. Experimental results demonstrate that the improved PSMC method performs better on tracking with little degradation on safety for different loads and human subjects. [ABSTRACT FROM AUTHOR]

Aimed at the nonlinearity and uncertainty of the manipulator system, a RBF (radial basis function) neural network-based fuzzy sliding-mode control method was proposed in this paper, in order to make the manipulator track the given trajectory at an ideal dynamic quality. In this method, the equivalent part of the sliding-mode control is approximated by the RBF neural network, in which no model information is required. Meanwhile, a fuzzy controller is developed to make adaptive adjustment of the sliding-mode control's switching gains according to the distance between the current motor point and the sliding-mode surface, thus effectively the problem of chattering is solved. This method has, to some extent, improved the performance of response and tracking, and reduced the time of adjustment and chattering of input control. The system stability is verified by Lyapunov's theorem. The simulation result suggests that the algorithm designed for the three-degree-of-freedom (3DOF) manipulator system is effective. [ABSTRACT FROM AUTHOR]

Currently upper limb exoskeleton rehabilitation robots powered by electric motors used in the hospitals are usually cumbersome, bulky and unmovable. Our developed RUPERT is a low-cost lightweight portable exoskeleton rehabilitation robot that can encourage stroke patients with high stiffness in arm flexor muscles to receive frequent intensive rehabilitation trainings in the community or home, but its joints are unidirectionally actuated by pneumatic artificial muscles (PAMs). RUPERT with one PAM of each joint is not suitable for stroke patients with weak muscles in the flaccid paralysis period. Functional electrical stimulation (FES) uses current with low frequency to activate paralyzed muscles, which can produce muscle torque and compensate the unidirectional drawbacks of RUPERT, so as to realize the two-way motion of its joints for passive reaching trainings. As both the exoskeleton robot driven by PAMs and neuromuscular skeletal system under FES possess the highly nonlinear and time-varying characteristics, which adds control difficulty to the hybrid dynamic system, iterative learning control (ILC) is chosen to control this newly designed hybrid rehabilitation system to realize repetitive task trainings. [ABSTRACT FROM AUTHOR]